Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	128,547
2	South Dakota	122,803
3	Rhode Island	110,164
4	Utah	109,171
5	Arizona	105,691
6	Tennessee	105,332
7	Wisconsin	102,320
8	Iowa	102,155
9	Oklahoma	99,642
10	Arkansas	99,552
11	Nebraska	99,373
12	Kansas	96,750
13	Alabama	94,847
14	Indiana	94,243
15	Mississippi	93,181
16	Idaho	91,914
17	Nevada	91,179
18	Wyoming	90,345
19	Illinois	89,729
20	Montana	88,783
21	South Carolina	87,554
22	Louisiana	87,385
23	California	85,203
24	Georgia	84,638
25	Texas	84,519
26	New Mexico	83,770
27	Kentucky	83,438
28	Minnesota	82,245
29	Florida	81,228
30	Delaware	81,110
31	Missouri	81,105
32	New Jersey	79,463
33	Ohio	77,570
34	Massachusetts	77,057
35	Alaska	74,427
36	North Carolina	74,344
37	New York	74,238
38	Connecticut	72,085
39	Colorado	69,763
40	West Virginia	68,337
41	Pennsylvania	67,125
42	Michigan	61,516
43	Virginia	60,141
44	Maryland	59,159
45	District of Columbia	52,708
46	New Hampshire	49,069
47	Washington	41,957
48	Puerto Rico	40,165
49	Oregon	34,284
50	Maine	29,930
51	Vermont	19,758
52	Hawaii	18,363

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Texas	867
2	Rhode Island	713
3	Connecticut	652
4	North Carolina	628
5	Arkansas	570
6	Arizona	490
7	Kansas	484
8	South Carolina	479
9	Kentucky	441
10	Georgia	420
11	New York	411
12	Oklahoma	405
13	Louisiana	402
14	Alabama	368
15	California	362
16	Florida	360
17	Utah	351
18	Massachusetts	342
19	New Jersey	336
20	Virginia	334
21	Delaware	311
22	Ohio	311
23	Iowa	306
24	Tennessee	294
25	Montana	289
26	Pennsylvania	289
27	Idaho	278
28	West Virginia	273
29	Colorado	267
30	Mississippi	259
31	Nevada	258
32	New Mexico	252
33	New Hampshire	251
34	Missouri	236
35	Indiana	231
36	Nebraska	230
37	Maine	225
38	Illinois	224
39	Wyoming	216
40	Alaska	210
41	Wisconsin	207
42	Vermont	194
43	Michigan	192
44	Oregon	172
45	Maryland	166
46	Washington	163
47	District of Columbia	154
48	South Dakota	146
49	North Dakota	133
50	Minnesota	115
51	Puerto Rico	108
52	Hawaii	50

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,442
2	New York	2,245
3	Massachusetts	2,133
4	Rhode Island	2,074
5	Mississippi	2,069
6	South Dakota	2,014
7	Connecticut	2,007
8	Louisiana	1,937
9	North Dakota	1,898
10	Arizona	1,867
11	Pennsylvania	1,719
12	Illinois	1,690
13	Alabama	1,672
14	Arkansas	1,651
15	New Mexico	1,592
16	Iowa	1,576
17	Michigan	1,565
18	Indiana	1,498
19	Tennessee	1,455
20	South Carolina	1,436
21	Nevada	1,420
22	Kansas	1,336
23	Georgia	1,332
24	Texas	1,317
25	District of Columbia	1,312
26	Florida	1,257
27	Missouri	1,220
28	Montana	1,220
29	Maryland	1,194
30	Delaware	1,160
31	West Virginia	1,148
32	Minnesota	1,117
33	Wisconsin	1,116
34	Wyoming	1,078
35	California	1,073
36	Nebraska	1,048
37	Colorado	1,000
38	Idaho	979
39	Ohio	977
40	Oklahoma	923
41	North Carolina	917
42	Kentucky	910
43	New Hampshire	791
44	Virginia	770
45	Washington	586
46	Puerto Rico	580
47	Utah	529
48	Oregon	476
49	Maine	466
50	Alaska	367
51	Hawaii	290
52	Vermont	286

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Alabama	35
2	South Carolina	22
3	Arizona	21
4	Montana	21
5	Tennessee	18
6	Missouri	17
7	Wyoming	16
8	Delaware	13
9	Kansas	13
10	Rhode Island	13
11	Arkansas	12
12	California	12
13	Mississippi	12
14	Georgia	11
15	Nevada	11
16	Texas	11
17	Connecticut	10
18	Louisiana	10
19	Maine	9
20	Oklahoma	9
21	Florida	8
22	New Mexico	8
23	New York	8
24	Alaska	7
25	Iowa	7
26	New Jersey	7
27	North Carolina	7
28	Ohio	7
29	Pennsylvania	7
30	District of Columbia	6
31	Kentucky	6
32	Massachusetts	6
33	West Virginia	6
34	Indiana	5
35	Maryland	5
36	Idaho	4
37	Illinois	4
38	New Hampshire	4
39	Virginia	4
40	Washington	4
41	Michigan	3
42	Oregon	3
43	Utah	3
44	Wisconsin	3
45	Colorado	2
46	Minnesota	2
47	Nebraska	2
48	Puerto Rico	2
49	Vermont	2
50	South Dakota	1
51	Hawaii	0
52	North Dakota	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	299,786	1	99
Chattahoochee	Georgia	252,223	2	99
Dewey	South Dakota	235,574	3	99
Lake	Tennessee	235,462	4	99
Lincoln	Arkansas	235,104	5	99
Davidson	Tennessee	122,747	232	92
Richland	South Carolina	87,406	1350	57
York	South Carolina	80,575	1702	45
Orange	California	78,254	1811	42
Pierce	Washington	39,287	2898	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Buffalo	South Dakota	6,626	3	99
Dickey	North Dakota	6,568	4	99
Iron	Wisconsin	6,506	5	99
Davidson	Tennessee	1,093	1892	39
Richland	South Carolina	1,073	1927	38
Orange	California	1,007	2026	35
York	South Carolina	947	2102	33
Pierce	Washington	527	2650	15

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.